基于几何结构特征的点云表面重建方法
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摘要
针对稠密点云存在噪声、城市场景具备规则几何结构等情况,提出了基于几何结构特征的点云表面重建方法。该方法利用RANSAC算法提取平面结构,将原始点云结构化为平面点、两个面相交的折点、三个以上平面相交的角点和散乱点;再对结构化后的点云进行三维Delaunay四面体剖分,使用四种点构成的三角面片改进最小割能量函数的惩罚项,实现了点云的表面重建。与几种点云表面重建经典算法和商业软件的比较实验结果表明,该方法可以很好地恢复场景中的平面等几何结构。
To solve the problems that the existing noise in dense point cloud generated from sequential images dense matching affects the accuracy, and geometric structure such as flat exists in urban scenes, a surface reconstruction method of dense point cloud based on geometric structure is proposed in this paper. RANSAC algorithm is applied to extract planar structure. The original points were structured into planar points, crease points of two planes intersecting, corner points of three or more planes intersecting and clutter points. Then the structured point cloud is divided into tetrahedron by 3D Delaunay, improving the penalty term of min-cut energy function by the triangular patches consisted by the four structured points to extract surface of the 3D Delaunay tetrahedron. The experiment results show that, compared with several classical methods and business software, the proposed method can reconstruct a real 3 D scene and can recovery the scene geometry structure of plane.
To solve the problems that the existing noise in dense point cloud generated from sequential images dense matching affects the accuracy, and geometric structure such as flat exists in urban scenes, a surface reconstruction method of dense point cloud based on geometric structure is proposed in this paper. RANSAC algorithm is applied to extract planar structure. The original points were structured into planar points, crease points of two planes intersecting, corner points of three or more planes intersecting and clutter points. Then the structured point cloud is divided into tetrahedron by 3D Delaunay, improving the penalty term of min-cut energy function by the triangular patches consisted by the four structured points to extract surface of the 3D Delaunay tetrahedron. The experiment results show that, compared with several classical methods and business software, the proposed method can reconstruct a real 3 D scene and can recovery the scene geometry structure of plane.
引文
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[10]Snavely N,Seitz S,Szeliski R.Photo tourism:exploring photo collections in 3D[J].ACM Transaction on Graphics(S0730-0301),2006,25(3):835-846.
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[12]Changchang Wu,Sameer Agarwal,Brian Curless,et al.Multicore bundle adjustment[C]//IEEE Computer Vision and Pattern Recognition,2011.
[13]魏英姿,刘晓莉.基于随机抽取一致性的稳健点云平面拟合[J].北京工业大学学报,2014,40(3):400-403.WEI Yingzi,LIU Xiaoli.Robust Plane Fitting of Point Cloud Based on RANSAC[J].Journal of Beijing University of Technology.2014,40(3):400-403.
[14]Tsai V J D.Delaunay triangulation in TIN creation:an oveview and linear time algorithm[J].International Journal of Geographical Information Science(S1365-8816),1993,7(6):501-524.
[15]D Attli,J D Boissonnat,A Lieutier.Complexity of the Delaunay triangulation of points on surfaces:the smooth case[C]//Nineteenth Symposium on Computational Geometry.ACM,2003:201-210.
[16]Labatut P,Pons J P,Keriven R.Efficient multi-view reconstruction of large-scale scenes using interest points,Delaunay triangulation and graph cuts[C]//IEEE International Conference on Computer Vision,Rio de Janeiro,2007.
[17]Labatut P,Pons J‐,Keriven R.Robust and Efficient Surface Reconstruction From Range Data[J].Computer Graphics Forum(S1467-8659),2010,28(8):2275-2290.
[18]Pix4d.Pix4D Support Site[EB/OL].https://support.pix4d.com/hc/en-us/sections/200591139-ExampleDatasets-Available-for-Download#gsc.tab=0,2015.
[19]CVLab.CVLab Data[EB/OL].http://cvlabwww.Epfl.ch/data/multiview/dense MVS.Html,2015.
[20]Boykov Y,Kolmogorov.An experimental comparison of min-cut/max-flow algorithms for energy minimization in vison[J].IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828),2004,26(9):1124-1137.
[21]Chen Zitan,Guevara J A.Systematic Selection of Very Im PORTANT Points from Digital Terrain Model for Constructing Triangular Irregular Net-Works[C]//Auto-Carto 8,Baltimore,USA,1987.
[22]M P Dubuisson,A K Jain.A modified Hausdorff distance for object matching[C]//The 12th IAPR International Conference,Jerusalem,1994.